Testing and evaluating device and testing method for dynamic performance of adsorption reaction

Abstract

The invention provides an adsorption reaction dynamic performance testing and evaluating device and a testing method.The testing and evaluating device comprises a gas supply module, a temperature control module and an adsorber module which are sequentially connected through a pipeline in the gas trend, and the inlet end and the outlet end of the adsorber module are connected with the same detection and analysis module; the outlet end of the detection analysis module is connected with an artificial intelligence control module. Through combined arrangement of all the modules, the adsorption performance of the adsorbent under a dynamic adsorption test can be tested and evaluated, a more accurate experimental basis is provided for design and development of actual industrial adsorbents and other porous media, and the test and evaluation device can also be expanded to research of a catalytic reaction process in a fixed bed reactor and has a wide application prospect. And testing and evaluation of the reaction kinetic model are realized.

Description

Technical Field

[0001] The invention belongs to the technical field of adsorbent adsorption performance testing, and relates to a testing and evaluation device and a testing method for the dynamic performance of an adsorption reaction. Background Art

[0002] The adsorption performance test and evaluation of adsorbents is of great significance for the development and optimization design of new and efficient adsorbents. At present, the characterization of adsorbent performance can be achieved by using pore structure parameters (specific surface area and pore size distribution, etc.) obtained by experiments such as specific surface area analyzers, and the adsorption performance under specific operating conditions (temperature, pressure, gas flow, etc.) can also be characterized by penetration curves.

[0003] However, it is difficult to test and evaluate the performance of adsorbents under given operating conditions (such as adsorption capacity and adsorption rate) using pore structure parameters, and the penetration curve is affected by particle stacking and other factors, and cannot accurately obtain the dynamic adsorption performance within the particles (such as adsorption rate, adsorption capacity, and changes in the thickness of the adsorption layer within the particles over time). When the adsorbent has a large content of micropores, the specific surface area is also large, and the theoretical adsorption capacity is also relatively large, but its adsorption rate is limited by micropore diffusion and is relatively low. Therefore, in order to develop adsorbents with large adsorption capacity and fast adsorption rate, it is urgent to establish a means to test and evaluate the dynamic adsorption performance of adsorbents. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a testing and evaluation device and testing method for the dynamic performance of adsorption reactions. In the present invention, through the combination of a gas supply module, a temperature control module, an adsorber module, a detection and analysis module and an artificial intelligence control module, the adsorption performance of the adsorbent under a dynamic adsorption test can be tested and evaluated, providing a more accurate experimental basis for the design and development of actual industrial adsorbents and other porous media. The testing and evaluation device can also be extended to the study of catalytic reaction processes in fixed bed reactors, thereby realizing the testing and evaluation of reaction kinetic models.

[0005] To achieve this object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a testing and evaluation device for the dynamic performance of an adsorption reaction, the testing and evaluation device comprising a gas supply module, a temperature control module and an adsorber module which are sequentially connected by pipelines along the direction of the gas, the inlet and outlet ends of the adsorber module being connected to the same detection and analysis module, the detection and analysis module being electrically connected to an artificial intelligence control module, the artificial intelligence control module being used to accurately judge and evaluate the test results of the detection and analysis module according to existing models or test results and to provide performance characterization results that are difficult to obtain through experiments.

[0007] In the present invention, through the combined setting of the gas supply module, the temperature control module, the adsorber module, the detection and analysis module and the artificial intelligence control module, the adsorption performance of the adsorbent under the dynamic adsorption test can be tested and evaluated, providing a more accurate experimental basis for the design and development of actual industrial adsorbents and other porous media, and the test and evaluation device can also be extended to the study of catalytic reaction processes in fixed bed reactors, to achieve the testing and evaluation of reaction kinetic models.

[0008] It should be noted that the artificial intelligence in the artificial intelligence control module of the present invention determines the internal model parameters according to the experimental data, and outputs some results that are difficult to obtain directly from experiments, and these results are very important for the evaluation of adsorption performance. In other words, some results are obtained by experiments, and another important result cannot be obtained by experiments but is given by the AI ​​model.

[0009] As a preferred technical solution of the present invention, the gas supply module includes independently arranged raw material gas supply source and regeneration gas supply source.

[0010] It should be noted that the specific gas supply components of the raw material gas supply source and the regeneration gas supply source in the present invention need to be adjusted according to the experimental conditions, which are not limited here and should be known to those skilled in the art.

[0011] Preferably, the outlet end of the raw material gas supply source and the outlet end of the regeneration gas supply source are connected to the inlet end of the temperature control module through a common pipeline.

[0012] Preferably, a first tail gas treatment system is provided between the temperature control module and the adsorber module.

[0013] Preferably, the outlet end of the adsorber module is connected to a second tail gas treatment system.

[0014] It should be noted that the present invention does not impose any special restrictions on the model, material and size of the first exhaust gas treatment system and the second exhaust gas treatment system. It can process the gas before / after adsorption, including combustion, chemical adsorption reaction and other harmless treatment. Technical personnel in this field can make adaptive adjustments according to actual conditions.

[0015] As a preferred technical solution of the present invention, the temperature control module includes an independently arranged low-temperature control module and a high-temperature control module, and the outlet end of the low-temperature control module and the outlet end of the high-temperature control module are connected to the collecting pipeline through a common pipeline.

[0016] Preferably, the cryogenic control module comprises a Dewar flask and a cryogenic heater which are sequentially connected by pipelines along the direction of the gas.

[0017] Preferably, cooling liquid is added into the Dewar flask.

[0018] Preferably, the cooling liquid is any one of liquid helium, liquid hydrogen, liquid nitrogen, liquid argon, liquid oxygen and liquid carbon dioxide.

[0019] Preferably, the high temperature control module is a high temperature heater.

[0020] As a preferred technical solution of the present invention, the outlet end of the collecting pipeline is independently connected to the first tail gas treatment system and the adsorber module.

[0021] Preferably, a first recuperator is provided between the collecting pipeline and the first tail gas treatment system.

[0022] As a preferred technical solution of the present invention, the low-temperature heater is used to heat the gas passing through the Dewar flask and regulate the temperature of the gas to be below 25°C, for example, it can be 25°C, 24°C, 23°C, 20°C, etc., but is not limited to the listed values, and other unlisted values ​​within the numerical range are also applicable.

[0023] Preferably, the temperature of the high temperature heater is 20-1000°C, for example, it can be 20°C, 50°C, 100°C, 200°C, 300°C, 400°C, 500°C, 600°C, 700°C, 800°C, 900°C, 1000°C, etc., but is not limited to the listed values, and other unlisted values ​​within the numerical range are also applicable.

[0024] Preferably, the temperature of the first reheater is 20-1000°C, for example, it can be 20°C, 50°C, 100°C, 200°C, 300°C, 400°C, 500°C, 600°C, 700°C, 800°C, 900°C, 1000°C, etc., but is not limited to the listed values, and other unlisted values ​​within the numerical range are also applicable.

[0025] The temperature of the first recuperator in the present invention is 20-1000° C., and the temperature can be adjusted according to the needs of tail gas treatment.

[0026] As a preferred technical solution of the present invention, the adsorber module includes an adsorption reactor and a vacuum pump which are sequentially connected by pipelines along the direction of the gas.

[0027] It should be noted that the adsorption reactor in the adsorber module of the present invention is equipped with an adsorbent and a vacuum pump, which can realize the regeneration of the adsorbent in the adsorber; or the addition of a catalyst can realize the detection of the changes of the gas before and after the reaction in the adsorber, thereby realizing the evaluation of the catalytic reaction performance. The present invention does not specifically limit the specific model, material and size of the adsorber and the vacuum pump, and those skilled in the art can make adaptive adjustments according to actual conditions.

[0028] Preferably, the adsorption reactor is externally connected to a gas flow meter, and the gas flow meter is used to measure the flow rate of the gas entering the adsorption reactor.

[0029] Preferably, a thermometer is installed on the adsorption reactor.

[0030] Preferably, an adsorbent or a catalyst is added into the adsorption reactor.

[0031] It should be noted that the present invention does not specifically limit the specific type, material, shape and particle size of the adsorbent or catalyst. Those skilled in the art can selectively adjust the type of adsorbent according to the relevant gas source; the adsorbent can be activated carbon, molecular sieve, metal organic framework (MOF) material, etc., and the catalyst can be molecular sieve, supported catalyst, etc.

[0032] Preferably, the branch between the adsorption reactor and the vacuum pump is connected to the detection and analysis module.

[0033] Preferably, the detection and analysis module is a gas detector, which is used to detect gas components and record result data.

[0034] It should be noted that the present invention does not specifically limit the model, material and size of the gas detector, and it only needs to detect and record the gas components. Those skilled in the art can make adaptive adjustments according to actual conditions. The detection and analysis module in the present invention can detect and record the components of the gas before adsorption within a certain temperature range and gas flow range; it can also detect the composition of the gas after it flows through the adsorbent in the adsorber, thereby obtaining a penetration curve (a curve showing the relationship between the concentration of each outflowing gas component and time when a single-component or multi-component mixed gas flows through the adsorbent) and record the result data.

[0035] As a preferred technical solution of the present invention, a second reheater and the second tail gas treatment system are connected in sequence along the direction of the gas between the adsorption reactor and the vacuum pump.

[0036] Preferably, the temperature of the second reheater is 20-1000°C, for example, it can be 20°C, 50°C, 100°C, 200°C, 300°C, 400°C, 500°C, 600°C, 700°C, 800°C, 900°C, 1000°C, etc., but is not limited to the listed values, and other unlisted values ​​within the numerical range are also applicable.

[0037] The temperature of the second reheater in the present invention is 20-1000° C., and the temperature can be adjusted according to the needs of tail gas treatment.

[0038] As a preferred technical solution of the present invention, the artificial intelligence control module includes an artificial intelligence processing module, an input module and an output module. The outlet end of the detection and analysis module is electrically connected to the artificial intelligence processing module and the input module in sequence, and the output module is electrically connected to the artificial intelligence processing module.

[0039] Preferably, the electrical connection is a data line connection.

[0040] It should be noted that the artificial intelligence processing module in the present invention is mainly an artificial intelligence (AI) model and algorithm module, including computer processing and data storage functions, and a built-in single-particle adsorption kinetic model of a certain number of typical porous particles, which contains multi-dimensional parameters (such as shapes such as spherical, ellipsoidal, cylindrical, clover, etc.; and parameters involved in the data input module); the AI ​​model and algorithm are derived from a large number of computational simulation results and existing experimental results, and can be updated later.

[0041] It should be noted that the input module in the present invention can input necessary data such as adsorbent properties, operating conditions, test results, etc. (the data of this module can be completely input manually, or part of it can be input, and the other part can be internally read from the thermometer, flow meter and data of the detection and analysis module, etc.), including but not limited to adsorbent particle shape, particle size, particle density, particle mass, porosity, pore size distribution, temperature, penetration curve data (discrete data of components, flow rate, component concentration changing with time) and adsorbent stacking thickness, etc.; it includes a data input and processing system (for example, capable of detecting data integrity and validity) and a data storage system.

[0042] It should be noted that the output module in the present invention can automatically select artificial intelligence models and algorithms according to the parameters input by the input module. Through these small amounts of experimental and calculation results and other data, it can provide the dynamic adsorption performance / adsorption kinetics under certain operating conditions and any combination of structural parameters within a certain range. For example, it can provide the distribution of adsorbed gas components inside a single particle under specific typical operating conditions, the equilibrium adsorption amount of a single particle, the theoretical adsorption amount, the utilization rate of adsorption sites, the adsorption thickness, the adsorption rate, and the adsorption kinetics of a single particle. It includes a data input and processing system, a data storage system, and a data output system (including text generation, printing, and visual display, etc.).

[0043] Preferably, the artificial intelligence processing module includes an artificial intelligence model and an algorithm module, and the artificial intelligence model and the algorithm module work in the following manner:

[0044] S1. Acquire experimental parameters, and obtain the parameters input by the input module through the artificial intelligence model and the algorithm module;

[0045] S2, inputting the parameters into a pre-trained target performance prediction model, evaluating the prediction model and determining parameter values ​​of the target performance prediction model;

[0046] S3. Obtain the target performance model and target performance value corresponding to the experiment through S2, and complete data storage.

[0047] Preferably, the experimental parameters include adsorbent particle shape parameters, adsorbent pore structure parameters and adsorber module parameters.

[0048] Preferably, the parameters input by the input module include temperature, pressure, adsorbent stacking thickness, inlet component concentration, inlet flow rate, outlet component concentration and outlet flow rate.

[0049] Preferably, the target performance values ​​include outlet component concentration, outlet flow rate, adsorption amount, adsorption rate, component concentration distribution in the adsorption reactor, conversion rate, selectivity, product yield, reaction rate of each component, evolution of the adsorption diffusion layer over time, residual adsorption capacity and single particle adsorption kinetics.

[0050] It should be noted that in the present invention, if a catalyst is added to the adsorption reactor, the test and evaluation is about the catalytic performance. At this time, the target performance includes the outlet component concentration, outlet flow rate, component concentration distribution in the adsorber, conversion rate, selectivity, product yield, reaction rate of each component and single particle reaction kinetics, etc.; if an adsorbent is added, the target performance includes the outlet component concentration, outlet flow rate, adsorption amount, adsorption rate, component concentration distribution in the adsorption reactor, evolution of the adsorption diffusion layer over time, residual adsorption capacity and single particle adsorption kinetics, etc.

[0051] Preferably, the specific operations of the AI ​​model and algorithm adopted by the target performance model are performed as follows:

[0052] Based on the open source Autogluon automatic machine learning framework, it uses ensemble learning methods to build and combine multiple learners to complete learning tasks. Autogluon uses all basic model types as stacked layers, and utilizes layered training to train multiple machine learning models simultaneously by using stacking technology, and then linearly weights multiple models as the final output; when the number of samples is limited, the k-fold cross-validation technology is used to divide the data set for training and verification.

[0053] It should be noted that Autogluon in the present invention specifically refers to the AutoML open source toolkit for deep learning launched by Amazon, which can be downloaded, installed and used for free and is a well-known software for those skilled in the art.

[0054] Preferably, the use of the k-fold cross-validation technique to divide the data set includes dividing the data set into k non-overlapping packages, each time using one of the packages as a test set and the remaining k-1 packages as training sets for training.

[0055] It should be noted that the specific number of "k" is not limited in the present invention, and those skilled in the art can make adaptive adjustments according to actual conditions.

[0056] In a second aspect, the present invention provides a testing method for the testing and evaluation device according to the first aspect, the testing method comprising:

[0057] The gas is supplied to the temperature control module and the adsorber module in turn through the gas supply module, the gas is processed by the first exhaust gas treatment system and the second exhaust gas treatment system respectively, and the gas is detected by the detection and analysis modules connected to the two ends of the adsorber module, and the detection results are uploaded to the artificial intelligence control module, which makes an accurate judgment on the detection results according to the existing model or test results.

[0058] As a preferred technical solution of the present invention, the testing method specifically includes:

[0059] (1) Preparation before adsorption: prepare the experimental gas of the raw gas supply source and the regeneration gas supply source, fill the adsorbent in the adsorption reactor and evacuate it by a vacuum pump;

[0060] (2) If it is low temperature adsorption, the experimental gas is cooled by passing through a Dewar flask, which contains cooling liquid, and the cooled gas is temperature controlled by a low temperature heater; if it is room temperature or high temperature adsorption, the experimental gas is directly temperature controlled by a high temperature heater;

[0061] (3) The gas after temperature control by the low temperature heater is sequentially processed by the first recuperator and the first tail gas treatment system; part of the gas after temperature control by the high temperature heater is detected by the gas detector for gas component detection and the result data is recorded, and the other part of the gas is sequentially processed by the first recuperator and the first tail gas treatment system;

[0062] (4) passing the temperature-controlled gas into the adsorption reactor for adsorption, a portion of the gas after passing through the adsorption reactor enters the gas detector for detection and recording, and the other portion is sequentially processed through the second reheater and the second tail gas treatment system. If the gas detector detects that the composition of the gas before the adsorption reactor and the gas after passing through the adsorption reactor is the same, the adsorption experiment is stopped;

[0063] (5) According to experimental requirements, the operating conditions such as temperature and pressure can be changed and the above operations (1) to (4) can be repeated;

[0064] (6) The input module inputs the physical property parameters of the adsorbent, which are uploaded to the artificial intelligence processing module through the data line to check and store the data, and then the data is output through the output module.

[0065] It should be noted that step (6) of the test method of the present invention may specifically be:

[0066] Input the physical property parameters of the adsorbent through the input module, including but not limited to the adsorbent particle shape, particle size, particle density, particle mass, porosity, pore size distribution, etc., and transmit them to the artificial intelligence processing and data storage system through the data line, and check and store the data validity at the same time; and input the operating conditions and other data to see whether they are obtained internally;

[0067] If the operating conditions are acquired internally, the artificial intelligence processing and data storage system reads and stores the temperature meter, flow meter, and gas component change data recorded during the experiment; if the operating conditions are manually input, the above data are read and stored through the input module;

[0068] According to the data of the input module, the artificial intelligence processing and data storage system will perform multi-parameter fitting and optimization processing according to the built-in AI model and algorithm, so as to obtain the dynamic adsorption performance / adsorption kinetics under any combination of operating conditions and structural parameters within a certain range. For example, it can give the distribution of adsorbed gas components inside a single particle under specific typical operating conditions, the equilibrium adsorption amount of a single particle, the theoretical adsorption amount, the utilization rate of adsorption sites, the adsorption thickness, the adsorption rate, and the adsorption kinetics of a single particle. Finally, the results are output through the output module in different forms such as text generation, printing, and visual display.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] In the present invention, through the combined setting of the gas supply module, the temperature control module, the adsorber module, the detection and analysis module and the artificial intelligence control module, the adsorption performance of the adsorbent under the dynamic adsorption test can be tested and evaluated, providing a more accurate experimental basis for the design and development of actual industrial adsorbents and other porous media, and the test and evaluation device can also be extended to the study of catalytic reaction processes in fixed bed reactors, to achieve the testing and evaluation of reaction kinetic models. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of the structure of a test and evaluation device provided in a specific embodiment of the present invention;

[0072] Among them, 1-raw material gas supply source; 2-regeneration gas supply source; 3-Dewar flask; 4-low temperature heater; 5-high temperature heater; 6-adsorption reactor; 7-gas detector; 8-second reheater; 9-second exhaust gas treatment system; 10-first reheater; 11-first exhaust gas treatment system; 12-vacuum pump; 13-artificial intelligence processing module; 14-input module; 15-output module. DETAILED DESCRIPTION

[0073] It should be understood that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0074] Those skilled in the art should understand that the present invention necessarily includes necessary pipelines, conventional valves and general pump equipment for realizing a complete process, but the above content does not belong to the main inventive point of the present invention. Those skilled in the art can add layouts on their own based on the process flow and equipment structure selection, and the present invention does not make special requirements and specific limitations on this.

[0075] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0076] In a specific embodiment, the present invention provides a test and evaluation device for the dynamic performance of an adsorption reaction, such as Figure 1 As shown, the test and evaluation device includes a gas supply module, a temperature control module and an adsorber module which are connected in sequence along the direction of the gas by pipelines; the inlet and outlet ends of the adsorber module are connected to the same detection and analysis module; the detection and analysis module is electrically connected to an artificial intelligence control module; the artificial intelligence control module is used to accurately judge and evaluate the test results of the detection and analysis module according to existing models or test results and to provide performance characterization results that are difficult to obtain through experiments; a first exhaust gas treatment system 11 is arranged between the temperature control module and the adsorber module, and the outlet end of the adsorber module is connected to a second exhaust gas treatment system 9.

[0077] In the present invention, through the combined setting of the gas supply module, the temperature control module, the adsorber module, the detection and analysis module and the artificial intelligence control module, the adsorption performance of the adsorbent under the dynamic adsorption test can be tested and evaluated, providing a more accurate experimental basis for the design and development of actual industrial adsorbents and other porous media, and the test and evaluation device can also be extended to the study of catalytic reaction processes in fixed bed reactors, to achieve the testing and evaluation of reaction kinetic models.

[0078] It should be noted that the present invention does not impose any special restrictions on the model, material and size of the first exhaust gas treatment system 11 and the second exhaust gas treatment system 9. The gas before and after adsorption can be treated, including combustion, chemical adsorption reaction and other harmless treatments. Those skilled in the art can make adaptive adjustments according to actual conditions.

[0079] It should be noted that the artificial intelligence in the artificial intelligence control module of the present invention determines the internal model parameters according to the experimental data, and outputs some results that are difficult to obtain directly from experiments, and these results are very important for the evaluation of adsorption performance. In other words, some results are obtained by experiments, and another important result cannot be obtained by experiments but is given by the AI ​​model.

[0080] Further, the gas supply module includes an independently arranged raw gas supply source 1 and a regeneration gas supply source 2. It should be noted that the specific gas supply components of the raw gas supply source 1 and the regeneration gas supply source 2 in the present invention need to be adjusted according to the experimental conditions, which is not limited here and should be known to those skilled in the art.

[0081] Furthermore, the outlet of the raw gas supply source 1 and the outlet of the regeneration gas supply source 2 are connected to the inlet of the temperature control module through a common pipeline. The temperature control module includes an independently set low-temperature control module and a high-temperature control module, and the outlet of the low-temperature control module and the outlet of the high-temperature control module are connected to the collective pipeline through a common pipeline.

[0082] Furthermore, the low temperature control module includes a dewar flask 3 and a low temperature heater 4 which are connected in sequence along the direction of the gas by pipelines, and a cooling liquid is added to the dewar flask 3. Furthermore, the cooling liquid is any one of liquid helium, liquid hydrogen, liquid nitrogen, liquid argon, liquid oxygen and liquid carbon dioxide. The high temperature control module is a high temperature heater 5.

[0083] Furthermore, the outlet end of the collecting pipeline is independently connected to the first tail gas treatment system 11 and the adsorber module, and a first recuperator 10 is arranged between the collecting pipeline and the first tail gas treatment system 11 .

[0084] Furthermore, the low-temperature heater 4 is used to heat the gas passing through the Dewar flask 3 and regulate the temperature of the gas to be below 25°C, for example, it can be 25°C, 24°C, 23°C, 20°C, etc., but is not limited to the listed values, and other unlisted values ​​within the numerical range are also applicable.

[0085] Furthermore, the temperature of the high temperature heater 5 is 20-1000°C, for example, it can be 20°C, 50°C, 100°C, 200°C, 300°C, 400°C, 500°C, 600°C, 700°C, 800°C, 900°C, 1000°C, etc., but is not limited to the listed values, and other unlisted values ​​within the numerical range are also applicable.

[0086] Furthermore, the temperature of the first reheater 10 is 20-1000°C, for example, it can be 20°C, 50°C, 100°C, 200°C, 300°C, 400°C, 500°C, 600°C, 700°C, 800°C, 900°C, 1000°C, etc., but is not limited to the listed values, and other unlisted values ​​within the numerical range are also applicable.

[0087] Furthermore, the adsorber module includes an adsorption reactor 6 and a vacuum pump 12 which are connected in sequence by pipelines along the direction of the gas. It should be noted that the adsorption reactor 6 in the adsorber module of the present invention is filled with adsorbent and is equipped with a vacuum pump 12, so that the adsorbent in the adsorber can be regenerated; or the addition of a catalyst can detect the changes of the gas before and after the reaction in the adsorber, thereby realizing the evaluation of the catalytic reaction performance. The present invention does not specifically limit the specific model, material, and size of the adsorber and vacuum pump 12, and those skilled in the art can make adaptive adjustments according to actual conditions.

[0088] Furthermore, the adsorption reactor 6 is externally connected to a gas flow meter for measuring the flow of gas entering the adsorption reactor 6. A thermometer is installed on the adsorption reactor 6. An adsorbent or a catalyst is added to the adsorption reactor 6. It should be noted that the present invention does not specifically limit the specific type, material, shape, and particle size of the adsorbent or the catalyst, and those skilled in the art can selectively adjust the type of the adsorbent according to the relevant gas source.

[0089] Furthermore, the branch between the adsorption reactor 6 and the vacuum pump 12 is connected to the detection and analysis module, and the detection and analysis module is a gas detector 7, which is used to detect the gas components and record the result data. It should be noted that the present invention does not specifically limit the model, material and size of the gas detector 7, as long as it can detect and record the gas components. Those skilled in the art can make adaptive adjustments according to actual conditions. The detection and analysis module in the present invention can detect and record the components of the gas before adsorption within a certain temperature range and gas flow range; it can also detect the composition of the gas after it flows through the adsorbent in the adsorber, thereby obtaining a penetration curve (a curve showing the relationship between the concentration of each outflowing gas component and time when a single-component or multi-component mixed gas flows through the adsorbent) and record the result data.

[0090] Furthermore, a second reheater 8 and a second exhaust gas treatment system 9 are connected in sequence along the direction of the gas between the adsorption reactor 6 and the vacuum pump 12. The temperature of the second reheater 8 is 20-1000°C, for example, it can be 20°C, 50°C, 100°C, 200°C, 300°C, 400°C, 500°C, 600°C, 700°C, 800°C, 900°C, 1000°C, etc., but is not limited to the listed values, and other unlisted values ​​within the numerical range are also applicable.

[0091] Furthermore, the artificial intelligence control module includes an artificial intelligence processing module 13, an input module 14 and an output module 15. The outlet end of the detection and analysis module is electrically connected to the artificial intelligence processing module 13 and the input module 14 in sequence. The output module 15 is electrically connected to the artificial intelligence processing module 13. Further, the electrical connection is a data line connection.

[0092] It should be noted that the artificial intelligence processing module 13 in the present invention is mainly an artificial intelligence (AI) model and algorithm module, including computer processing and data storage functions, and a built-in single-particle adsorption kinetic model of a certain number of typical porous particles, which contains multi-dimensional parameters (such as shapes of sphere, ellipsoid, column, clover, etc.; and parameters involved in the data input module 14); the AI ​​model and algorithm are derived from a large number of computational simulation results and existing experimental results, and can be updated later.

[0093] It should be noted that the input module 14 in the present invention can input necessary data such as adsorbent properties, operating conditions, test results, etc. (the data of this module can be completely input manually, or part of it can be input, and the other part can be internally read from the thermometer, flow meter and data of the detection and analysis module, etc.), including but not limited to adsorbent particle shape, particle size, particle density, particle mass, porosity, pore size distribution, temperature, penetration curve data (discrete data of components, flow rate, component concentration changing with time) and adsorbent stacking thickness, etc.; it includes a data input and processing system (for example, capable of detecting data integrity and validity) and a data storage system.

[0094] It should be noted that the output module 15 in the present invention can automatically select artificial intelligence models and algorithms according to the parameters input by the input module 14. Through these small amounts of experimental and calculation results and other data, it can provide the dynamic adsorption performance / adsorption kinetics under certain operating conditions and any combination of structural parameters within a certain range. For example, it can provide the distribution of adsorbed gas components inside a single particle under specific typical operating conditions, the equilibrium adsorption amount of a single particle, the theoretical adsorption amount, the utilization rate of adsorption sites, the adsorption thickness, the adsorption rate, and the adsorption kinetics of a single particle. It includes a data input and processing system, a data storage system, and a data output system (including text generation, printing, and visual display, etc.).

[0095] Furthermore, the artificial intelligence processing module 13 includes an artificial intelligence model and an algorithm module, and the artificial intelligence model and the algorithm module work in the following manner:

[0096] S1, obtaining experimental parameters, and obtaining parameters input by the input module 14 through the artificial intelligence model and algorithm module;

[0097] S2. Input the parameters into a pre-trained target performance prediction model, evaluate the prediction model and determine the parameter values ​​of the target performance prediction model;

[0098] S3. Obtain the target performance model and target performance value corresponding to the experiment through S2, and complete data storage.

[0099] Furthermore, the experimental parameters include adsorbent particle shape parameters, adsorbent pore structure parameters and adsorber module parameters.

[0100] Furthermore, the parameters input by the input module 14 include temperature, pressure, adsorbent stacking thickness, inlet component concentration, inlet flow rate, outlet component concentration and outlet flow rate.

[0101] Furthermore, the target performance values ​​include outlet component concentration, outlet flow rate, adsorption amount, adsorption rate, component concentration distribution in the adsorption reactor 6, conversion rate, selectivity, product yield, reaction rate of each component, evolution of the adsorption diffusion layer with time, residual adsorption capacity and single particle adsorption kinetics.

[0102] It should be noted that in the present invention, if a catalyst is added to the adsorption reactor 6, the test and evaluation is about the catalytic performance. At this time, the target performance includes the outlet component concentration, outlet flow rate, component concentration distribution in the adsorber, conversion rate, selectivity, product yield, reaction rate of each component and single particle reaction kinetics, etc.; if an adsorbent is added, the target performance includes the outlet component concentration, outlet flow rate, adsorption amount, adsorption rate, component concentration distribution in the adsorption reactor 6, evolution of the adsorption diffusion layer over time, residual adsorption capacity and single particle adsorption kinetics, etc.

[0103] Furthermore, the specific operations of the AI ​​model and algorithm adopted by the target performance model are performed as follows:

[0104] Based on the open source Autogluon automatic machine learning framework, it uses ensemble learning methods to build and combine multiple learners to complete learning tasks. Autogluon uses all basic model types as stacked layers, and utilizes layered training to train multiple machine learning models simultaneously by using stacking technology, and then linearly weights multiple models as the final output; when the number of samples is limited, the k-fold cross-validation technology is used to divide the data set for training and verification.

[0105] Furthermore, using the k-fold cross validation technique to divide the data set includes dividing the data set into k non-overlapping packages, using one of the packages as a test set each time, and the remaining k-1 packages as training sets for training. It should be noted that the specific number of "k" is not limited in the present invention, and those skilled in the art can make adaptive adjustments according to actual conditions.

[0106] In another specific embodiment, the present invention provides a testing method for the above-mentioned testing and evaluation device, the testing method comprising:

[0107] The gas supply module supplies gas to the temperature control module and the adsorber module in turn, and the gas is processed by the first exhaust gas treatment system 11 and the second exhaust gas treatment system 9 respectively. The gas is detected by the detection and analysis modules connected to the two ends of the adsorber module, and the detection results are uploaded to the artificial intelligence control module, which makes an accurate judgment on the detection results according to the existing model or test results.

[0108] Furthermore, the test method specifically includes:

[0109] (1) Preparation before adsorption: prepare the experimental gas of the raw gas supply source 1 and the regeneration gas supply source 2, fill the adsorbent in the adsorption reactor 6 and evacuate it by the vacuum pump 12;

[0110] (2) If it is low-temperature adsorption, the experimental gas is cooled by the Dewar flask 3, which contains cooling liquid, and the cooled gas is temperature-controlled by the low-temperature heater 4; if it is room-temperature or high-temperature adsorption, the experimental gas is directly temperature-controlled by the high-temperature heater 5;

[0111] (3) The gas after temperature control by the low temperature heater 4 is sequentially processed by the first recuperator 10 and the first tail gas treatment system 11; part of the gas after temperature control by the high temperature heater 5 is detected by the gas detector 7 for gas component and the result data is recorded, and the other part of the gas is sequentially processed by the first recuperator 10 and the first tail gas treatment system 11;

[0112] (4) The temperature-controlled gas is passed into the adsorption reactor 6 for adsorption. A portion of the gas after passing through the adsorption reactor 6 enters the gas detector 7 for detection and recording, and the other portion is sequentially processed through the second reheater 8 and the second tail gas treatment system 9. If the gas detector 7 detects that the composition of the gas before the adsorption reactor 6 and the gas after passing through the adsorption reactor 6 is the same, the adsorption experiment is stopped;

[0113] (5) According to experimental requirements, the operating conditions such as temperature and pressure can be changed and the above operations (1) to (4) can be repeated;

[0114] (6) The input module 14 inputs the physical property parameters of the adsorbent, which are uploaded to the artificial intelligence processing module 13 through the data line to check and store the data, and then the data is output through the output module 15.

[0115] It should be noted that step (6) of the test method of the present invention may specifically be:

[0116] Input the physical property parameters of the adsorbent through the input module 14, including but not limited to the adsorbent particle shape, particle size, particle density, particle mass, porosity, pore size distribution, etc., and transmit them to the artificial intelligence processing and data storage system through the data line, and check and store the data validity at the same time; and input the operating conditions and other data to see whether they are obtained internally;

[0117] If the operating conditions are acquired internally, the artificial intelligence processing and data storage system reads and stores the temperature meter, flow meter, and gas component change data recorded during the experiment; if the operating conditions are manually input, the above data are read and stored through the input module 14;

[0118] According to the data of the input module 14, the artificial intelligence processing and data storage system will perform multi-parameter fitting and optimization processing according to the built-in AI model and algorithm, so as to obtain the dynamic adsorption performance / adsorption kinetics under any combination of operating conditions and structural parameters within a certain range, such as the distribution of adsorbed gas components inside a single particle under specific typical operating conditions, the equilibrium adsorption amount of a single particle, the theoretical adsorption amount, the utilization rate of adsorption sites, the adsorption thickness, the adsorption rate, and the adsorption kinetics of a single particle. Finally, the results are output through the output module 15, which can be in different forms such as text generation, printing, and visual display.

[0119] Example 1

[0120] This embodiment provides a device and method for testing and evaluating the dynamic performance of an adsorption reaction, wherein:

[0121] The test and evaluation device includes a gas supply module, a temperature control module and an adsorber module which are connected in sequence by pipelines along the direction of the gas; the inlet and outlet ends of the adsorber module are connected to the same detection and analysis module; the detection and analysis module is electrically connected to an artificial intelligence control module; the artificial intelligence control module is used to accurately judge and evaluate the test results of the detection and analysis module based on existing models or test results and to provide performance characterization results that are difficult to obtain through experiments; a first exhaust gas treatment system 11 is arranged between the temperature control module and the adsorber module, and the outlet end of the adsorber module is connected to a second exhaust gas treatment system 9.

[0122] The gas supply module includes an independently arranged raw material gas supply source 1 and a regeneration gas supply source 2. The outlet end of the raw material gas supply source 1 and the outlet end of the regeneration gas supply source 2 are connected to the inlet end of the temperature control module by a common pipeline, wherein the gas used by the raw material gas supply source 1 can be a mixed gas of nitrogen, oxygen, chlorine and neon. The temperature control module includes an independently arranged low temperature control module and a high temperature control module, and the outlet end of the low temperature control module and the outlet end of the high temperature control module are connected to the aggregate pipeline by a common pipeline.

[0123] The low temperature control module includes a dewar flask 3 and a low temperature heater 4 connected in sequence along the direction of the gas, and liquid nitrogen is added to the dewar flask 3; the high temperature control module is a high temperature heater 5. The outlet end of the summary pipeline is independently connected to the first tail gas treatment system 11 and the adsorber module, and a first recuperator 10 is arranged between the summary pipeline and the first tail gas treatment system 11. The low temperature heater 4 is used to heat the gas passing through the dewar flask 3 and adjust the temperature of the gas to below 25°C, the temperature of the high temperature heater 5 is 600°C, and the temperature of the first recuperator 10 is 200°C.

[0124] The adsorber module includes an adsorption reactor 6 and a vacuum pump 12 connected in sequence along the gas flow. The adsorption reactor 6 is externally connected to a gas flow meter for measuring the flow of gas entering the adsorption reactor 6. A thermometer is installed on the adsorption reactor 6. Activated carbon is added to the adsorption reactor 6.

[0125] The branch between the adsorption reactor 6 and the vacuum pump 12 is connected to the detection and analysis module, which is a gas detector 7. The gas detector 7 is used to detect the gas components and record the result data. The second reheater 8 and the second tail gas treatment system 9 are also connected to the adsorption reactor 6 and the vacuum pump 12 along the gas flow. The temperature of the second reheater 8 is 200°C.

[0126] The artificial intelligence control module includes an artificial intelligence processing module 13, an input module 14 and an output module 15. The outlet end of the detection and analysis module is electrically connected to the artificial intelligence processing module 13 and the input module 14 in sequence. The output module 15 is electrically connected to the artificial intelligence processing module 13. Furthermore, the electrical connection is a data line connection.

[0127] The artificial intelligence processing module 13 includes an artificial intelligence model and an algorithm module, which work in the following manner:

[0128] S1, obtaining experimental parameters, and obtaining parameters input by the input module 14 through the artificial intelligence model and algorithm module;

[0129] S2. Input the parameters into a pre-trained target performance prediction model, evaluate the prediction model and determine the parameter values ​​of the target performance prediction model;

[0130] S3. Obtain the target performance model and target performance value corresponding to the experiment through S2, and complete data storage.

[0131] The experimental parameters include adsorbent particle shape parameters, adsorbent pore structure parameters and adsorber module parameters. The parameters input by the input module 14 include temperature, pressure, adsorbent stacking thickness, inlet component concentration, inlet flow rate, outlet component concentration and outlet flow rate. The target performance values ​​include outlet component concentration, outlet flow rate, adsorption amount, adsorption rate, component concentration distribution in the adsorption reactor 6, evolution of the adsorption diffusion layer over time, residual adsorption capacity and single particle adsorption kinetics.

[0132] The specific operations of the AI ​​model and algorithm used in the target performance model are as follows:

[0133] Based on the open source Autogluon automatic machine learning framework, it uses ensemble learning methods to build and combine multiple learners to complete learning tasks. Autogluon uses all basic model types as stacked layers, and utilizes layered training to train multiple machine learning models simultaneously by using stacking technology, and then linearly weights multiple models as the final output; when the number of samples is limited, the k-fold cross-validation technology is used to divide the data set for training and verification.

[0134] Using the k-fold cross validation technique to divide the data set includes dividing the data set into k non-overlapping packages, using one of the packages as a test set each time, and the remaining k-1 packages as training sets for training. It should be noted that the specific number of "k" is not limited in the present invention, and those skilled in the art can make adaptive adjustments according to actual conditions.

[0135] The test method of the test evaluation device includes:

[0136] The gas is supplied to the temperature control module and the adsorber module in turn through the gas supply module. The gas is processed by the first exhaust gas treatment system 11 or the second exhaust gas treatment system 9, and the gas is detected by the detection and analysis modules connected to the two ends of the adsorber module. The detection results are uploaded to the artificial intelligence control module, and the artificial intelligence control module makes an accurate judgment on the detection results according to the existing model or test results.

[0137] Specifically:

[0138] (1) Preparation before adsorption: prepare the experimental gas of the raw gas supply source 1 and the regeneration gas supply source 2, fill the adsorbent in the adsorption reactor 6 and evacuate it by the vacuum pump 12;

[0139] (2) If it is low-temperature adsorption, the experimental gas is cooled by the Dewar flask 3, which contains cooling liquid, and the cooled gas is temperature-controlled by the low-temperature heater 4; if it is room-temperature or high-temperature adsorption, the experimental gas is directly temperature-controlled by the high-temperature heater 5;

[0140] (3) The gas after temperature control by the low temperature heater 4 is sequentially processed by the first recuperator 10 and the first tail gas treatment system 11; part of the gas after temperature control by the high temperature heater 5 is detected by the gas detector 7 for gas component and the result data is recorded, and the other part of the gas is sequentially processed by the first recuperator 10 and the first tail gas treatment system 11;

[0141] (4) The temperature-controlled gas is passed into the adsorption reactor 6 for adsorption. A portion of the gas after passing through the adsorption reactor 6 enters the gas detector 7 for detection and recording, and the other portion is sequentially processed through the second reheater 8 and the second tail gas treatment system 9. If the gas detector 7 detects that the composition of the gas before the adsorption reactor 6 and the gas after passing through the adsorption reactor 6 is the same, the adsorption experiment is stopped;

[0142] (5) According to experimental requirements, the operating conditions such as temperature and pressure can be changed and the above operations (1) to (4) can be repeated;

[0143] (6) The input module 14 inputs the physical property parameters of the adsorbent, which are uploaded to the artificial intelligence processing module 13 through the data line to check and store the data, and then the data is output through the output module 15.

[0144] In summary, the present invention, through the combination of the gas supply module, the temperature control module, the adsorber module, the detection and analysis module and the artificial intelligence control module, can react to the adsorption performance of the adsorbent under the dynamic adsorption test, and provide a more accurate experimental basis for the design and development of actual industrial adsorbents and other porous media. The test and evaluation device can also be extended to the study of catalytic reaction processes in fixed bed reactors, and realize the testing and evaluation of reaction kinetic models.

[0145] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention are within the protection scope and disclosure scope of the present invention.

Claims

1. A device for testing and evaluating the dynamic performance of an adsorption reaction, characterized in that: The test and evaluation device includes a gas supply module, a temperature control module and an adsorber module which are connected in sequence by pipelines along the direction of the gas. The inlet and outlet ends of the adsorber module are connected to the same detection and analysis module. The detection and analysis module is electrically connected to an artificial intelligence control module. The artificial intelligence control module is used to accurately judge and evaluate the test results of the detection and analysis module based on existing models or test results and to provide performance characterization results that are difficult to obtain through experiments.

2. The test evaluation device according to claim 1, characterized in that: The gas supply module includes a raw material gas supply source and a regeneration gas supply source which are independently arranged; Preferably, the outlet end of the raw material gas supply source and the outlet end of the regeneration gas supply source are connected to the inlet end of the temperature control module through a common pipeline.

3. The test evaluation device according to claim 1 or 2, characterized in that: The temperature control module includes a low temperature control module and a high temperature control module which are independently arranged, and the outlet end of the low temperature control module and the outlet end of the high temperature control module are connected to a collection pipeline through a common pipeline; Preferably, the cryogenic control module comprises a Dewar flask and a cryogenic heater which are sequentially connected by pipelines along the direction of the gas, and the Dewar flask is used to add cooling liquid; Preferably, the cooling liquid is any one of liquid helium, liquid hydrogen, liquid nitrogen, liquid argon, liquid oxygen and liquid carbon dioxide; Preferably, the high temperature control module is a high temperature heater; Preferably, a first tail gas treatment system is provided between the temperature control module and the adsorber module; Preferably, the outlet end of the adsorber module is connected to a second tail gas treatment system.

4. The test evaluation device according to claim 3, characterized in that: The outlet ends of the collecting pipeline are independently connected to the first tail gas treatment system and the adsorber module respectively; Preferably, a first recuperator is provided between the collecting pipeline and the first tail gas treatment system.

5. The test evaluation device according to claim 4, characterized in that: The low temperature heater is used to heat the gas passing through the Dewar flask and regulate the temperature of the gas to be below 25°C; Preferably, the temperature of the high temperature heater is 20 to 1000°C; Preferably, the temperature of the first recuperator is 20-1000°C.

6. The test and evaluation device according to any one of claims 3 to 5, characterized in that: The adsorber module includes an adsorption reactor and a vacuum pump connected in sequence by pipelines along the direction of the gas; Preferably, the adsorption reactor is externally connected to a gas flow meter, and the gas flow meter is used to measure the flow rate of the gas entering the adsorption reactor; Preferably, a thermometer is installed on the adsorption reactor; Preferably, an adsorbent or a catalyst is added into the adsorption reactor; Preferably, a branch between the adsorption reactor and the vacuum pump is connected to the detection and analysis module; Preferably, the detection and analysis module is a gas detector, which is used to detect gas components and record result data.

7. The test evaluation device according to claim 6, characterized in that: A second reheater and the second tail gas treatment system are connected in turn along the gas flow between the adsorption reactor and the vacuum pump; Preferably, the temperature of the second reheater is 20-1000°C.

8. The test and evaluation device according to any one of claims 1 to 7, characterized in that: The artificial intelligence control module includes an artificial intelligence processing module, an input module and an output module. The output end of the detection and analysis module is electrically connected to the artificial intelligence processing module and the input module in sequence, and the output module is electrically connected to the artificial intelligence processing module. Preferably, the electrical connection is a data line connection; Preferably, the artificial intelligence processing module includes an artificial intelligence model and an algorithm module, and the artificial intelligence model and the algorithm module work in the following manner: S1. Acquire experimental parameters, and obtain the parameters input by the input module through the artificial intelligence model and the algorithm module; S2, inputting the parameters into a pre-trained target performance prediction model, evaluating the prediction model and determining parameter values ​​of the target performance prediction model; S3, obtaining the target performance model and target performance value corresponding to the experiment through S2, and completing data storage; Preferably, the experimental parameters include adsorbent particle shape parameters, adsorbent pore structure parameters and adsorber module parameters; Preferably, the parameters input by the input module include temperature, pressure, adsorbent stacking thickness, inlet component concentration, inlet flow rate, outlet component concentration and outlet flow rate; Preferably, the target performance values ​​include outlet component concentration, outlet flow rate, adsorption amount, adsorption rate, component concentration distribution in the adsorption reactor, conversion rate, selectivity, product yield, reaction rate of each component, evolution of the adsorption diffusion layer over time, residual adsorption capacity and single particle adsorption kinetics; Preferably, the specific operations of the AI ​​model and algorithm adopted by the target performance model are performed as follows: Based on the open source Autogluon automatic machine learning framework, it uses ensemble learning methods to build and combine multiple learners to complete learning tasks. Autogluon uses all basic model types as stacked layers, and uses layered training to train multiple machine learning models simultaneously by using stacking technology, and then linearly weights multiple models as the final output; when the number of samples is limited, the k-fold cross-validation technology is used to divide the data set for training and verification; Preferably, the use of the k-fold cross-validation technique to divide the data set includes dividing the data set into k non-overlapping packages, each time using one of the packages as a test set and the remaining k-1 packages as training sets for training.

9. A testing method for a testing and evaluation device according to any one of claims 1 to 8, characterized in that: The test method includes: The gas is supplied to the temperature control module and the adsorber module in turn through the gas supply module, the gas is processed by the first exhaust gas treatment system and the second exhaust gas treatment system respectively, and the gas is detected by the detection and analysis modules connected to the two ends of the adsorber module, and the detection results are uploaded to the artificial intelligence control module, which makes an accurate judgment on the detection results according to the existing model or test results.

10. The testing method according to claim 9, characterized in that: The test method specifically includes: (1) Preparation before adsorption: prepare the experimental gas of the raw gas supply source and the regeneration gas supply source, fill the adsorbent in the adsorption reactor and evacuate it by a vacuum pump; (2) If it is low temperature adsorption, the experimental gas is cooled by passing through a Dewar flask, which contains cooling liquid, and the cooled gas is temperature controlled by a low temperature heater; if it is room temperature or high temperature adsorption, the experimental gas is directly temperature controlled by a high temperature heater; (3) The gas after temperature control by the low temperature heater is sequentially processed by the first recuperator and the first tail gas treatment system; part of the gas after temperature control by the high temperature heater is detected by the gas detector for gas component detection and the result data is recorded, and the other part of the gas is sequentially processed by the first recuperator and the first tail gas treatment system; (4) passing the temperature-controlled gas into the adsorption reactor for adsorption, a portion of the gas after passing through the adsorption reactor enters the gas detector for detection and recording, and the other portion is sequentially processed through the second reheater and the second tail gas treatment system. If the gas detector detects that the composition of the gas before the adsorption reactor and the gas after passing through the adsorption reactor is the same, the adsorption experiment is stopped; (5) According to experimental requirements, the operating conditions such as temperature and pressure can be changed and the above operations (1) to (4) can be repeated; (6) The input module inputs the physical property parameters of the adsorbent, which are uploaded to the artificial intelligence processing module through the data line to check and store the data, and then the data is output through the output module.

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